Tasks / Strategy

Challenge an idea

Can the model find the strongest reason an idea may fail, backed by evidence?

Task type v1.2 · 2 tasksLast changed 6 Oct 2026 · ChangelogDifficulty

What AI gets right here, and what you’ll still have to catch

From 16 graded outputs by 8 models. 44% were usable with at most a quick edit.

Reliably right

  1. Finds the load-bearing assumption100% pass
    The critique targets the assumption that agencies' growth is limited by a shortage of new leads, using referral, abandoned-outbound, and capacity evidence.
    GPT-6.1 Sol · API · An AI SDR for small agencies
  2. Uses the interviews faithfully100% pass
    All quotes are accurately taken from the transcripts and correctly attributed to A05, A04, and A08.
    GPT-6.1 Sol · API · An AI SDR for small agencies
  3. Finds the timeline that sinks it100% pass
    Cites 9-month build and concludes Q1 launch is unsupported.
    GPT-6 Astra · ChatGPT · Five billion dollars, on paper

Where it slips

  1. Avoids unsupported claims55% pass
    Presents the existence of local taxes and multi-state complexity as fact without support from the context.
    GPT-6 Astra · ChatGPT · Five billion dollars, on paper
  2. Uses the supplied evidence correctly55% pass
    Claims that local taxes and multi-state operations affect eligibility are not supported by the supplied context, which only mentions state-by-state tax rules.
    GPT-6 Astra · ChatGPT · Five billion dollars, on paper
  3. Rebuilds the market size81% pass
    It uses the $150 price for 10+ staff businesses instead of the higher UK revenue per customer for those segments, so the market size is understated and not correctly rebuilt.
    GPT-6 Luna · API · Five billion dollars, on paper

How it’s graded

Each check is built on how world-class product and growth leaders do this work, as they’ve described it on Lenny’s Podcast, and names the leader it comes from.

  1. Finds the need behind the idea

    Does the critique identify the underlying need the idea is meant to serve, and offer at least one other way to meet that need, at a different cost, saying what would have to be dropped for it?

    Passes when States the need behind the proposal, and gives an alternative way to meet it with its cost and what it displaces.

Plus our standard checks

Uses the supplied evidence correctly · Addresses the actual decision · Respects explicit constraints · Identifies material uncertainty · Avoids unsupported claims · Produces the required deliverable · Tigers, not paper tigers · and 2 written for each task, which you’ll see in the tasks below.

The tasks

Read the brief, then compare up to three outputs, each with the LLM judge’s verdict on every check. Highlights mark what a PM had to fix.

The brief

You're the VP Product at Halyard. Our CEO is taking the US launch plan below to the board next week and has asked you to challenge it hard first. Write a memo of no more than 1,000 words: what the plan gets wrong, what would sink it, what's less scary than it looks, and what you'd propose instead. The deck excerpt and our data are attached.

What the model was given4 items: about_halyard.md, ceo_board_deck_excerpt.md, uk_customers_by_size.csv, us_small_business_stats.md
about_halyard.md3 lines · Download
# About Halyard in the UK

Payroll and HR for small businesses. 14,800 customers, $3.9M ARR, five years old. UK payroll rules are built in; US payroll would need state-by-state tax rules (the team estimates 9 months of work for the first 10 states).
ceo_board_deck_excerpt.md5 lines · Download
# US launch (CEO's board deck, excerpt)

- Market: 33 million US small businesses x our UK price of £120 ($150) a year = about $5B. "Even 1% is $50M."
- Plan: launch in the US in Q1, $2.5M budget, hire a US sales team of 6, target $5M ARR in year one.
- Why now: a competitor raised in the US; we need to get there first.
uk_customers_by_size.csv4 rows · Download
company_size,uk_customers,share_of_uk_customers_pct,retained_at_12_months_pct,avg_revenue_usd_per_year
Sole trader,6100,41,18,150
2-9 staff,5300,36,33,150
10-49 staff,2900,20,71,620
50-249 staff,480,3,78,2400
us_small_business_stats.md5 lines · Download
# US small businesses (government statistics)

- 33.2 million small businesses in total.
- About 27.1 million have no employees (sole proprietors).
- About 6.1 million have employees; of those, about 1.1 million have 10 to 249 employees.
What a strong answer doesThe answer key the graders mark against

Shows the $5B market is the wrong number. It multiplies every US small business by the UK price, but in the UK only businesses with 10+ staff stay (71% and 78% at 12 months) and pay more ($620 to $2,400 a year); sole traders and 2-9 businesses mostly leave (18% and 33%). Most US small businesses (27.1M of 33.2M) have no employees and don't need payroll at all; the comparable US market is about 1.1M businesses with 10 to 249 staff, worth roughly $0.7B to $1B at UK revenue per customer, not $5B. The tiger: US payroll needs state-by-state tax rules, about 9 months for the first 10 states, so a Q1 launch with payroll isn't possible and $5M ARR in year one would need thousands of 10+ staff customers from a standing start. Dismisses the 'first mover' worry as less scary: a competitor's raise doesn't make a market winner-takes-all. Proposes instead: target 10+ staff businesses, start with HR in the US while payroll is built for a few high-density states, test demand with a small budget and a clear threshold before hiring a sales team, and fix the UK mix towards 10+ staff companies meanwhile.

Critical failures (cap the score at 40)
  • Endorses the $5B market size
Task

Staff-level · v1.0

The outputs

8 setups answered this brief.

Usable with a quick edit8 of 10 checks passed

The memo is well-reasoned and actionable; the unsupported local-taxes claim is minor and can be quickly removed or hedged without redoing the analysis.

Output

To: CEO and Board From: VP Product Subject: US expansion: approve validation, not the proposed launch

Recommendation: Do not approve the plan as written. The US may be attractive, but the deck mistakes business counts for demand, imports our weakest UK economics, and commits to distribution before we can reliably deliver the product. Approve a staged validation programme instead, with compliance and commercial gates before releasing the full $2.5M.

What the plan gets wrong

1. The addressable market is not 33 million payroll buyers. Of 33.2 million US small businesses, 27.1 million have no employees. Some owner-only businesses may need payroll, but they are not equivalent to employer businesses. The relevant starting population is approximately 6.1 million employers—and probably the 1.1 million with 10–249 employees, given our UK results.

“Even 1%” is not a strategy. It assumes a reachable segment, competitive differentiation, distribution and retention that we have not demonstrated.

2. Our own data contradicts the proposed price and customer strategy.

UK segmentCustomers12-month retentionAnnual revenue/customer
------:---:---:
Sole trader6,10018%$150
2–9 staff5,30033%$150
10–49 staff2,90071%$620
50–249 staff48078%$2,400

Businesses with 10–249 staff represent approximately 23% of customers but 63% of the annual revenue implied by this table. Their weighted retention is approximately 72%, versus 25% for customers below ten staff. Our strongest evidence favours larger employers, not maximum account volume at $150.

Using our UK customer mix within 10–249 staff gives approximately $873 annual revenue per customer. Applied to 1.1 million US businesses, that suggests a roughly $960M segment revenue pool—not a forecast, and not all serviceable by a ten-state product. US willingness to pay remains untested.

There is also a board-data problem: the table implies $4.66M annual revenue, versus the stated $3.9M ARR. Reconcile definitions, dates and cohorts before presenting either figure as the economic baseline.

3. The sales plan and revenue target do not fit. At $150 annually, $5M ARR requires approximately 33,333 active customers: 463 per salesperson per month across six reps over a full year, before churn. Even allocating the entire $2.5M budget to acquisition would allow only $75 per active customer, leaving nothing for engineering, compliance or support.

At the illustrative $873 blend, we still need approximately 5,730 active customers—80 per rep per month. These are workload checks, not sales forecasts, but neither resembles a credible unproven, sales-led launch. A later launch compresses the selling window further.

4. Competitive fundraising is not customer evidence. A competitor’s financing may raise acquisition costs or accelerate competition. It does not establish that our product is ready or that customers will switch. “Get there first” is especially weak when the competitor is already there.

What would sink it

Payroll reliability and coverage. The team estimates nine months for the first ten states. A Q1 launch is unsupported unless substantial work is already complete or we use a proven partner. Ten states also do not automatically mean ten self-contained markets: employee locations, local taxes and multi-state operations affect eligibility. Incorrect wages, filings or remittances can destroy trust faster than sales can rebuild it.

Scaling the wrong economics. At our smallest-customer retention levels, acquisition fills a leaking bucket. Higher retention among larger employers is promising, but customer retention is not revenue retention or gross margin. We lack US acquisition cost, onboarding effort, support burden and contribution-margin evidence.

Starving the UK business. The proposed budget is approximately 64% of current ARR—not a cash-runway calculation, but a material commitment. We need an explicit runway and UK-roadmap impact assessment, including engineering diversion and downside scenarios.

What is less scary than it looks

We do not need nationwide coverage or a large share of the US to build something valuable. A tightly qualified segment can support a meaningful business.

Nor must we necessarily build every payroll component ourselves. A payroll infrastructure partner could shorten time to market, although coverage, margins, accountability and vendor dependence require diligence.

Finally, weak microbusiness retention does not prove Halyard cannot travel. The larger-employer cohorts provide a credible starting hypothesis. They do not yet prove US product-market fit.

What I would propose instead

1. Release up to $400,000 for a 90-day validation phase; hold the remainder. Reconcile the financial baseline, analyse retention by cohort and acquisition channel, and compare partner versus internal-build economics and timelines.

2. Start with 10–49 employees. Recruit 10–20 design partners with simple, geographically supported payroll needs. Validate switching triggers, differentiation, pricing and onboarding. Keep 50–249 as a subsequent hypothesis: better UK retention may come with substantially greater implementation complexity.

3. Gate live payroll on readiness, not the calendar. Require documented coverage, independent compliance review, successful parallel payroll testing, and clear incident ownership. No unsupported employee locations.

4. Run a 30–50-customer paid pilot before hiring six sellers. Use founder-led sales and limited implementation capacity. Measure acquisition cost, onboarding time, support cost, contribution margin and retention. Seek evidence supporting acquisition payback within 12–18 months; early retention is provisional until cohorts mature.

5. Return for scale funding only after those gates. Replace the $5M year-one promise with a bottom-up forecast based on verified price, conversion, launch timing, capacity and churn. The board should fund the next proven step—not an unsupported percentage of America.

Claims the judge couldn’t find in the brief

  • Employee locations, local taxes and multi-state operations affect eligibility.

Grades and run details

Decision model 100 · LLM judge 82
Decision model checks
  • passUses the supplied evidence correctly29%
  • passAddresses the actual decision99%
  • passRespects explicit constraints59%
  • passIdentifies material uncertainty99%
  • passAvoids unsupported claims40%
  • passProduces the required deliverable87%
  • passRebuilds the market size100%
  • passFinds the timeline that sinks it99%
  • passTigers, not paper tigers85%
  • passFinds the need behind the idea44%
Run
Run
#1
Time to output
51 s
Submitted
5 Oct 2026

Usable with a quick edit8 of 10 checks passed

Minor unsupported claim about crowded market can be edited out; core reasoning and evidence use are solid.

Output

MEMO

To: CEO From: VP Product Re: Challenge to the US launch plan before the board

Bottom line: The market story is wrong, the timeline is not achievable, and the budget can't carry the target. The opportunity is real but much smaller and more specific than $5B. I'd take the board a staged plan with gates, not a Q1 launch.

---

What the plan gets wrong

1. The TAM is built on the wrong customers. 33M × $150 assumes every US small business buys our product at our UK sole-trader price. But 27.1M of those firms have no employees. Payroll is not their problem. Our own data shows sole traders retain at 18% at twelve months and bring in $150 a year. Our customers who retain are the ones with staff.

2. The right pool is about 1.1M firms, and the "1%" math shrinks accordingly. The government figure is 1.1M businesses with 10–249 employees. Our UK revenue is concentrated in the 10–49 and 50–249 segments: about 23% of customers but roughly 60%+ of revenue, with 71–78% retention. Taking a blended ~$620 ARPU, 1% of 1.1M firms is about 11,000 customers and roughly $7M ARR, not $50M. The deck's own logic, applied to the right segment, gives a much smaller prize.

3. The $5M year-one target is not credible. At $620 ARPU, $5M needs about 8,000 paying US customers in twelve months. It took us five years to reach 14,800 UK customers, with a product that is already built. At $150, it needs 33,000, which only works if we sell to the segment that churns most.

4. Q1 is not feasible. The US needs state-by-state payroll tax rules. Our team estimates nine months for the first ten states alone. Q1 launch means either launching with almost no state coverage or launching with a product we can't legally run for most prospects. A firm with employees in an unsupported state cannot onboard at all.

5. "Get there first" is the wrong reason. US payroll is a crowded market with entrenched incumbents, and a competitor's raise means they face the same state-tax build we do. Being early doesn't create a moat in payroll. Being correct on compliance does.

6. The budget doesn't match the plan. Six US quota-carrying reps at a fully loaded cost of roughly $150–180K each is about $1M a year before marketing, tax build, legal, compliance, or US support. That leaves very little of the $2.5M for everything else. (These are my estimates. Please check them against Finance's hiring costs.)

---

What would sink it

  • Compliance liability. Wrong withholding or filing in a state is our error, with penalties landing on our customers. One public failure in an early state would damage referrals across the whole US business.
  • Selling to the wrong segment. If sales pursues sole traders and 2–9 employee firms because the TAM looks big, we'll be acquiring customers at $150 ARPU who churn at 67–82% within a year. CAC payback will never work.
  • Hiring ahead of proof. Six reps with quotas before we know which buyer, which price, and which state set will produce a pipeline we can't convert.
  • Support load. Multi-state questions are hard, and US support can't be staffed from London on a launch timeline.
  • Burn without a gate. If US spend starts before UK growth is secure, a US miss would hit our core business's cash position.

---

What's less scary than it looks

  • The product already works where it matters. Our best UK segments retain at 71–78%. We don't need a new product thesis, just a US version of what works.
  • The tax build is bounded. Nine months for ten states is a real cost, but it's finite. Ten states cover roughly half the US population, so coverage is a sequencing problem. We could also evaluate licensing state tax content from a vendor to cut the timeline.
  • The competitor raise is validation. It shows investors believe in US SMB payroll demand. It also shows we'd be competing in a market where a credible challenger still needs years to build coverage.
  • We don't need a Q1 launch to have a US business. A focused pilot can prove demand within two quarters at a fraction of the cost.

---

What I'd propose instead

I'd ask the board to approve a staged program with explicit gates.

Redefine the market in the deck. The target customer is a US business with 10–249 employees. That's about 1.1M firms and a credible multi-million-dollar opportunity, and we should say so.

Stage 1: Validate (months 0–6, ~$300K). Sign 40–60 design-partner firms with 10–249 employees across two states, one large state and one with simpler rules. Test activation, willingness to pay at $600+ ARPU, and 90-day engagement. Also test a partner channel: accountants and bookkeepers serve our best-fit firms and may be cheaper to reach than direct sales.

Stage 2: Build (months 6–15, ~$800K). Build or license state coverage for the top ten states. Hire one or two sales and customer success people, with the founder leading enterprise-style deals. Gate: pilot cohort retention tracking to 70%+ and CAC payback under 18 months.

Stage 3: Scale (only after gates). Expand sales headcount and marketing against measured unit economics.

Revised target: $1M US ARR by month 24, which is roughly 1,600 customers at $620 ARPU. That is ambitious but defensible, and it's the number the board should hold us to.

The ask: Approve Stages 1 and 2 (about $1.1M) now, with Stage 3 contingent on gate review. Do not approve a $2.5M Q1 launch with a $5M year-one target.

---

I know this is a harder message than the deck. But a board that approves the current plan will be disappointed in twelve months. A board that approves a gated path will get real evidence before committing the big money. I'd rather take the second case to them.

Claims the judge couldn’t find in the brief

  • US payroll is a crowded market with entrenched incumbents.

Grades and run details

Decision model 85 · LLM judge 82
Decision model checks
  • failUses the supplied evidence correctly22%
  • passAddresses the actual decision99%
  • passRespects explicit constraints50%
  • passIdentifies material uncertainty94%
  • partialAvoids unsupported claims66%
  • passProduces the required deliverable90%
  • passRebuilds the market size98%
  • passFinds the timeline that sinks it100%
  • passTigers, not paper tigers57%
  • passFinds the need behind the idea61%
Run
Run
#1
API response time
31 s
Submitted
8 Oct 2026

Check by check

Where the outputs differ comes first. Open a check to read the judge’s reasons. “Mixed” means the two graders disagreed.

Where they differ 2

Uses the supplied evidence correctlyMixedWrong
GPT-6 Astra · ChatGPT

Claims that local taxes and multi-state operations affect eligibility are not supported by the supplied context, which only mentions state-by-state tax rules.

Haiku 5.5 · API

Claim that US payroll is a crowded market with entrenched incumbents is unsupported by supplied context.

Avoids unsupported claimsMixedWrong
GPT-6 Astra · ChatGPT

Presents the existence of local taxes and multi-state complexity as fact without support from the context.

Haiku 5.5 · API

States the US payroll market is crowded with entrenched incumbents as a fact without evidence or hedging.

All got right 8

Addresses the actual decisionRightRight
GPT-6 Astra · ChatGPT

Commits to not approving the plan and proposes a staged validation, with clear conditions for scaling.

Haiku 5.5 · API

Commits to a staged plan with gates, rejecting Q1 launch, framed for CEO, with conditions for scaling.

Respects explicit constraintsRightRight
GPT-6 Astra · ChatGPT

Memo format, under 1,000 words, addressed to CEO and board.

Haiku 5.5 · API

Memo is within 1,000 words and addressed to CEO as requested.

Identifies material uncertaintyRightRight
GPT-6 Astra · ChatGPT

Identifies untested US willingness to pay, acquisition costs, and retention, and proposes validation to resolve them.

Haiku 5.5 · API

Identifies unknowns (buyer, price, state set, build time) and uses gates to resolve them with measurable triggers.

Produces the required deliverableRightRight
GPT-6 Astra · ChatGPT

Complete memo with actionable recommendation, within length, usable with light edits.

Haiku 5.5 · API

Memo format, to CEO, actionable, and within the word limit.

Rebuilds the market sizeRightRight
GPT-6 Astra · ChatGPT

Rebuilds market using employer counts and UK retention, arriving at ~$960M for 1.1M businesses.

Haiku 5.5 · API

Uses employer counts and retention data to show relevant market is ~1.1M firms, 1% worth ~$7M ARR, not $50M, with working.

Finds the timeline that sinks itRightRight
GPT-6 Astra · ChatGPT

Cites 9-month build and concludes Q1 launch is unsupported.

Haiku 5.5 · API

Names the 9-month build and explains that Q1 launch is infeasible without state coverage.

Tigers, not paper tigersRightRight
GPT-6 Astra · ChatGPT

Separates sinkers (payroll reliability, scaling wrong economics) from less scary (competitor raise, nationwide coverage), and surfaces ARR discrepancy.

Haiku 5.5 · API

Ranks risks, dismisses competitor raise as less scary, and surfaces TAM churn error as the hidden sink.

Finds the need behind the ideaRightRight
GPT-6 Astra · ChatGPT

Identifies need for US expansion, proposes validation instead of full launch, with cost ($400k) and what is displaced.

Haiku 5.5 · API

States the opportunity is real and offers staged validation as an alternative, displacing the full Q1 $2.5M launch.

Results

Every setup we’ve tested on this task type, across all its tasks and repeats, graded on the current checklist. Calibrated. Where our PM checked the graders by hand, they agreed on 76% of 75 checks.

#Model · HarnessTask scoreDecision modelLLM judgeRunsCritical failures
1GPT-6 LunawithAPI100.090.92None
2Haiku 5.5withAPI92.590.92None
3GPT-6.1 SolwithAPI100.086.42None
4GPT-6 AstrawithChatGPT100.079.42None
5Sonnet 5.5withAPI88.374.82None
6Opus 5.5withClaude88.379.421 capped
7Gemini 3.5 Flash-LitewithGemini79.649.721 capped
8Gemini 3.8 FlashwithAPI87.972.721 capped

What you get for the cost

No setup scores within 5 points of the leader at half its cost or less here, so the leader is also the best value.

Task score against relative cost for each setup. The table below has the same figures.4050607080901001×3×10×30×Cost per output, relative to the cheapest setup (log scale) →↑ Task scoreGPT-6 Luna · API: task score 93, the cheapestHaiku 5.5 · API: task score 92, the cheapestGPT-6.1 Sol · API: task score 91, 11× the cheapestGPT-6 Astra · ChatGPT: task score 88, 60× the cheapest (1 of 2 outputs estimated)Sonnet 5.5 · API: task score 78, 37× the cheapestOpus 5.5 · Claude: task score 62, 59× the cheapest (1 of 2 outputs estimated), 1 critical failureGemini 3.5 Flash-Lite · Gemini: task score 50, 1.9× the cheapest (1 of 2 outputs estimated), 1 critical failureGemini 3.8 Flash · API: task score 50, 9.5× the cheapest, 1 critical failureGPT-6 Luna · APIHaiku 5.5 · APIGPT-6.1 Sol · APIGPT-6 Astra · ChatGPTSonnet 5.5 · APIOpus 5.5 · ClaudeGemini 3.5 Flash-Lite · GeminiGemini 3.8 Flash · API
Cost partly estimated (app runs with no usage meter)At least one critical failure
Task score and relative cost by setup
SetupTask scoreRelative cost (cheapest = 1)
GPT-6 Luna · API931.0×
Haiku 5.5 · API921.4×
Gemini 3.5 Flash-Lite · Gemini501.9× (partly estimated)
Gemini 3.8 Flash · API509.5×
GPT-6.1 Sol · API9111×
Sonnet 5.5 · API7837×
Opus 5.5 · Claude6259× (partly estimated)
GPT-6 Astra · ChatGPT8860× (partly estimated)

Cost is each output’s tokens at the model’s list price, shown relative to the cheapest setup. Runs done in an app have no usage meter, so theirs are estimated from the same setup’s measured runs. How we compare cost

About the task

The PM job

Pressure-testing a proposal before committing a team to it.

Why it matters

The useful critic finds the one assumption everything rests on. Theatrical negativity is easy to generate and useless in a planning meeting.

What good looks like

  • Identifies the load-bearing assumption
  • Separates the risks that could kill it from the ones that only look scary
  • Uses the supplied evidence, not generic risks
  • Proposes the cheapest way to test the assumption

Deliberately not measured

  • Tone
  • Number of objections raised
Capability tested

Evidence-based critique

The failure we’re looking for

Theatrical negativity without evidence

Grading

Decision model and LLM judge, calibrated against a blind PM review

Variants

Vanilla prompt (core) · With Roast Me skill · Staff level: a company bet with a long evidence pack